1. Field of the Invention
The present invention relates to a method and apparatus for tracking moving objects in real time whereby trajectories corresponding to the movement of the objects are determined. More particularly, the present invention relates to a method and apparatus for tracking moving objects, such as balls, pucks, and the like, in connection with sporting events and exploiting such tracking to derive information corresponding to the movement of the objects being tracked. The present invention particularly relates to a method and apparatus for tracking an object such as a tennis ball.
2. Description of Related Art
One field in which real time tracking would be particularly desirable, but is not currently greatly utilized, is in the field of sports. For example, continuous tracking of a tennis ball during a tennis match provides valuable information about the skill and strategy of a player because information such as ball speed and ball placement would be readily obtainable therefrom. The trajectory of the ball, obtained through such real time tracking can be used to obtain other information of interest, as well as form the basis for virtual camera views of play from any desired position, as well as form the basis for virtual replays of play.
However, the only conventional form of tennis ball tracking currently available is a radar gun used to measure speed of service.
In addition, real time tracking of objects such as athletes or balls in a sporting event is challenging, especially because it is difficult to obtain a clean segmentation of the ball from the background in view of changing lighting conditions, variations in clothing worn by athletes (especially with regard to color), differences in the characteristics (for example, reflectivity) of playing surfaces (grass, clay, hardwood, ice, etc.), and the fast and dynamic movement of athletes. Another factor is the presence of other moving objects or moving people (such as other players on the field, spectators, and the like).
Also, in the case of tracking a ball or the like, fundamentally, the ball is a relatively very small object (approximately 6.5 cm in diameter) travelling at relatively high speed (up to 67 m/s) over a relatively large area of a tennis court (24 m long by 11 m wide) in the presence of other, much larger moving objects (the players).
Conventionally, tracking systems for moving objects typically generate trajectories corresponding to the motion of an object within the view of a camera. The trajectories or tracks typically consist of a sequence of x,y (location) coordinates and time coordinates. The information from these trajectories has a variety of applications. For example, the information can be used to count the number of objects, such as a people or vehicles, crossing a reference line and to associate a particular direction with each crossing. In addition, such trajectories may be used to determine the number of people present within the field of view of a camera at any instant, which information is useful, for example, for product marketing such as determining the effectiveness of a particular advertisement or advertising technique in a store. Tracking systems may also be employed for measuring consumer traffic throughout, for example, the aisles of a store, etc., including the length of time that particular persons spend in specific aisles.
Several methods or systems have been developed for the tracking of moving objects, including people. However, these conventional systems do not yield a single motion region or even a consistent set of motion regions, which deficiencies are exacerbated when tracking athletes, balls, pucks, and like in the midst of highly dynamic movement.
For example, in Rashid, R. F., "Towards A System For The Interpretation Of Moving Light Displays", 2 IEEE Transactions on Pattern Analysis and Machine Intelligence, 574-581 (1980), a method is described for interpreting moving light displays (MLD). In general, Rashid teaches segmenting out from MLD images individual points corresponding to moving people. The individual points are grouped together to form clusters based on, inter alia, the positions and velocities of the individual points; the formed clusters represented individual objects. Tracking is performed by matching points between consecutive frames based on the relative distances between the location of points in the current frame and the location of predicted points in a previous frame. The predicted position is based on the average velocity of the point in the previous frame and the relative distance, which is calculated using a Euclidean function.
The technique described by Rashid has several drawbacks. Specifically, the MLD system requires several frames before a good object separation is obtained, and no criteria is provided for determining when satisfactory object separation has occurred. In addition, no mechanism is provided for propagating the generated clusters to prior and subsequent frames for continuity in the motion representation. This undermines real time operation.
In another tracking system described in Rossi, M. and Bozzoli, A., "Tracking And Counting Moving People", Proceedings Of The Second IEEE International Conference On Image Processing, 212-16 (1994), a vertically mounted camera is employed for tracking and counting moving people. This system operates under the assumption that people enter a scene along either the top or bottom of the image where altering zones are positioned for detecting people moving into the scene. In reality, however, people can also appear in a scene, inter alia, from behind another object or from behind an already-identified person. In other words, people may be wholly or partially occluded upon initially entering a scene and would not be identified by this system. The problem of identifying occluded persons is also present in the system described in Rohr, K., "Towards Model Based Recognition Of Human Movements In Image Sequences", 59 Computer Vision, Graphics And Image Processing: Image Understanding, 94-115 (1994). Such problems are clearly pertinent to real time tracking of a sporting event such as tennis.
In addition, the systems described in Smith, S. M., and Brady, J. M., "A Scene Segmenter: Visual Tracking of Moving Vehicles", 7 Engineering Applications Of Artificial Intelligence 191-204 (1994); and "ASSET-2: Real-Time Motion Segmentation And Shape Tracking", 17 IEEE Transactions On Pattern Analysis And Machine Intelligence, 814-20 (1995), are designed specifically for tracking objects such as moving vehicles, and accordingly identify features representing corners or abrupt changes on the boundaries of the vehicles.
By definition, this precludes use with balls and the like, which do not have corners or abrupt changes on their boundaries.
In U.S. patent application Ser. No. 08/586,012, filed on Dec. 29, 1995, an apparatus and method are disclosed for tracking moving objects in real time. In particular, an apparatus and method are disclosed in which local features, such as extrema of curvature on boundary contours, are tracked, and trajectories of motion are derived by dynamically clustering the paths of motion of the local features.